Dynamic medical supply procurement

Abstract

A method for dynamically obtaining supplies includes storing identification information of supplies used during the medical procedure in main memory 115, monitoring information from the medical procedure during the medical procedure, predicting, by execution of instructions by processor 152 and based on the monitoring information, whether missing supplies will need to be obtained during the medical procedure, and obtaining the missing supplies during the medical procedure based on the predicted need to obtain the missing supplies.

Description

[Background technology]

[0001]

[0001] The cost of cardiovascular disease to the U.S. economy may have exceeded $500 billion in 2017 and is projected to reach $1.1 trillion by 2035. As much as 30% of these costs can be attributed to waste and inefficiencies in healthcare, such as inefficient supply chains and waste in surgery. Avoidable costs of cardiovascular disease are found in clinical workflows such as the catheterization lab, where consumable devices account for a significant cost of the procedure. Summary of the Invention [Problem to be solved by the invention]

[0002]

[0002] Healthcare costs in the United States are driving increased operational efficiency and improved inventory distribution while maintaining or improving clinical outcomes. Predicting supply usage in advance can be difficult. Furthermore, if a supply is in short supply in one ward and / or floor of a hospital, staff may have to obtain the supply from another ward and / or floor, or even from a different hospital, which can lead to increased patient wait times, even if the patient is in a critical condition or undergoing surgery. Supply usage may also vary depending on the preferences and capabilities of medical professionals who are in a position to use the supplies. Improved clinical outcomes depend on the ability to customize supply procurement according to staff preferences and capabilities, as well as the condition of the patient, to achieve improved clinical outcomes. Additionally, the price of supplies may change over time and may differ from alternatives that can be used with equally acceptable clinical outcomes. [Means for solving the problem]

[0003]

[0003] According to an aspect of the present disclosure, a method for dynamically obtaining supplies includes storing identification information of supplies used during a medical procedure in a main memory; monitoring information from the medical procedure during the medical procedure; predicting, by a processor executing instructions and based on the monitoring information, whether a missing supply will need to be obtained during the medical procedure; and obtaining the missing supply during the medical procedure based on the predicted need to obtain the missing supply.

[0004]

[0004] According to another aspect of the disclosure, a system for dynamically acquiring supplies includes a central computer and a main memory. The central computer includes a first memory storing first instructions and a first processor executing the first instructions. The main memory stores identification information of supplies used during a medical procedure. When executed by the first memory, the first instructions cause the central computer to monitor information from the medical procedure during the medical procedure, predict whether there is a need to acquire missing supplies during the medical procedure based on monitoring the information, and acquire the missing supplies during the medical procedure based on the predicted need to acquire the missing supplies.

[0005]

[0005] According to another aspect of the disclosure, a controller includes a memory and a processor. The memory stores instructions. The processor executes the instructions. When executed by the processor, the instructions cause the controller to obtain identification information of supplies used during a medical procedure, monitor information from the medical procedure during the medical procedure, predict whether a missing supply will need to be obtained during the medical procedure based on the monitoring information, and obtain the missing supply during the medical procedure based on the predicted need to obtain the missing supply. [Brief description of the drawings]

[0006]

[0006] Example embodiments are best understood when the following detailed description is read in conjunction with the accompanying drawing figures. It is emphasized that the various features are not necessarily drawn to scale. In fact, dimensions have been arbitrarily increased or reduced for clarity of discussion. Where applicable and practical, like reference numerals refer to like elements.

[0007] [Figure 1A] FIG. 1A illustrates a system for dynamic medical supply procurement according to a representative embodiment. [Figure 1B]

[0008] FIG. 1B illustrates another system for dynamic medical supply procurement in accordance with a representative embodiment. [Figure 1C]

[0009] FIG. 1C illustrates another system for dynamic medical supply procurement in accordance with a representative embodiment. [Figure 1D]

[0010] FIG. 1D illustrates a controller for dynamic medical supply procurement according to a representative embodiment. [Figure 1E]

[0011] FIG. 1E illustrates an overview of a system for dynamic medical supply procurement according to a representative embodiment. [Diagram 2]

[0012] FIG. 2 illustrates a method for dynamic medical supply procurement according to a representative embodiment. [Figure 3A]

[0013] FIG. 3A illustrates a method for dynamic medical supply procurement according to a representative embodiment. [Figure 3B]

[0014] FIG. 3B illustrates insight generation for dynamic medical supply procurement according to a representative embodiment. [Figure 4]

[0015] FIG. 4 illustrates a computer system upon which a method for dynamic medical supply procurement according to another representative embodiment is implemented. [Figure 5A]

[0016] FIG. 5A illustrates a user interface for an administrator / provider computer for dynamic medical supply procurement according to a representative embodiment. [Figure 5B]

[0017] FIG. 5B illustrates another user interface for an administrator / provider computer for dynamic medical supply procurement according to a representative embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008]

[0018] In the following detailed description, for purposes of explanation and not limitation, representative embodiments disclosing specific details are described to provide a thorough understanding of the embodiments according to the present teachings. Descriptions of known systems, devices, materials, methods of operation, and methods of manufacture may be omitted so as not to obscure the description of the representative embodiments. However, systems, devices, materials, and methods within the purview of one of ordinary skill in the art are within the scope of the present teachings and may be used in accordance with the representative embodiments. It is to be understood that the terms used herein are for the purpose of describing particular embodiments only and are not intended to be limiting. Defined terms are to be given the technical and scientific meaning of the defined terms as commonly understood and accepted in the art of the present teachings.

[0009]

[0019] In this specification, terms such as "first," "second," and "third" are used to describe various elements or components, but it should be understood that these elements or components are not limited by these terms. These terms are used only to distinguish one element or component from another. Thus, a first element or component discussed below may be referred to as a second element or component without departing from the teachings of the inventive concept.

[0010]

[0020] The terms used herein are for the purpose of describing particular embodiments only and are not intended to be limiting. As used in this specification and the appended claims, singular terms are intended to include both the singular and the plural, unless the context clearly indicates otherwise. Additionally, as used herein, the term "comprises" and / or similar terms specify the presence of the stated features, elements, and / or components, but do not exclude the presence or addition of one or more other features, elements, components, and / or groups thereof. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0011]

[0021] Unless otherwise noted, when an element or component is said to be "connected," "coupled," or "adjacent" to another element or component, it is understood that the element or component may be directly connected or coupled to the other element or component, or there may be intervening elements or components present. That is, these and similar terms encompass the cases where one or more intermediate elements or components may be used to connect the two elements or components. However, when an element or component is said to be "directly connected" to another element or component, this only encompasses the cases where the two elements or components are connected to each other without any intermediate or intervening elements or components.

[0012]

[0022] The present disclosure is intended to provide one or more of the advantages specifically set forth below through one or more of its various aspects, embodiments, and / or specific features, subcomponents. For purposes of explanation and not limitation, example embodiments disclosing specific details are described to provide a thorough understanding of the embodiments according to the present teachings. However, other embodiments consistent with the present disclosure that depart from the specific details disclosed herein remain within the scope of the appended claims. Furthermore, descriptions of well-known devices and methods may be omitted so as not to obscure the description of the example embodiments. Such methods and devices are within the scope of the present disclosure.

[0013]

[0023] As described herein, inventory management systems can be complemented and enhanced by generating and leveraging insights regarding supplies used in clinical procedures by different staff to provide real-time forecasts and suggestions. The ability to provide real-time forecasts and suggestions benefits from information obtained from clinical procedures, such as clinical, operational, and / or workflow information. This can provide healthcare providers with a comprehensive overview of supply usage, staff preferences, adherence to guidelines, specific trends, and opportunities to improve performance.

[0014]

[0024] FIG. 1A illustrates a system 100A for dynamic medical supply procurement according to a representative embodiment.

[0015]

[0025] 1A is a system for dynamic medical supply procurement that includes components that may be provided together or distributed. System 100A includes a central computer 110, a main memory 115, an administrator / provider computer 120, an imaging system 170, and a display 180.

[0016]

[0026] The central computer 110 may be a server computer, a desktop computer, or another type of computer. The central computer 110 includes a memory for storing instructions and a processor for executing instructions. The central computer 110 may include a single computer or a set of multiple cooperative computers, such as a data center. Although a computer used to implement the central computer is illustrated in FIG. 4, the central computer 110 may include more or less elements than those illustrated in FIG. 4. The central computer 110 may perform some or most aspects of the methods described herein.

[0017]

[0027] The main memory 115 includes a non-volatile memory, such as a flash memory. The main memory 115 stores one or more types of information from previous medical procedures previously performed. The main memory 115 stores past information of one or more staff members, such as past medical procedures they were involved in and the types of supplies they used during the past medical procedures. The main memory 115 may include a single unified memory or may include a set of multiple, cooperating memories.

[0018]

[0028] Administrator / provider computer 120 may be a desktop computer, a laptop computer, or another type of computer. Administrator / provider computer 120 includes a memory for storing instructions and a processor for executing instructions. A computer used to implement administrator / provider computer 120 is shown in FIG. 4, although administrator / provider computer 120 may include more or less elements than those shown in FIG. 4. Administrator / provider computer 120 is representative of a computer used by administrators and providers to enter data and retrieve data from main memory 115. For example, administrator / provider computer 120 may be used to enter user-defined rules that central computer 110 uses when predicting whether supplies that are out of stock during a medical procedure will need to be obtained during the medical procedure.

[0019]

[0029] The imaging system 170 is a medical imaging system, such as an ultrasound system or an x-ray system. The imaging system 170 may include a set of multiple different medical imaging systems, such as ultrasound systems and x-ray systems. The imaging system 170 generates medical images during a medical procedure and provides the medical images and / or information derived from the medical images to the central computer 110. The medical images from the imaging system 170 may be analyzed to predict when to obtain missing supplies. The analysis of the medical images from the imaging system 170 may involve the quality of the medical images, such as whether the imaging system 170 is generating medical images of acceptable quality. The analysis of the medical images from the imaging system 170 may also include medical analysis, such as whether the medical images show anatomical characteristics consistent with expectations for the medical procedure.

[0020]

[0030] The display 180 may be local to the imaging system 170, local to the central computer 110, and / or local to the administrator / provider computer 120. The display 180 may include multiple different displays. The display 180 may be connected to the central computer 110 via a local wired interface, such as an Ethernet cable, or via a local wireless interface, such as a Wi-Fi connection. The display 180 may also be interfaced with other user input devices, such as a mouse, keyboard, thumb wheel, etc., through which a user can input instructions.

[0021]

[0031] Display 180 is a monitor such as a computer monitor, an augmented reality display, a television, an electronic whiteboard, or another screen that displays electronic images. Display 180 may also include one or more input interfaces that connect other elements or components to central computer 110, and an interactive touch screen that displays prompts to and collects touch input from the user.

[0022]

[0032] FIG. 1B illustrates another system for dynamic medical supply procurement in accordance with a representative embodiment.

[0023]

[0033] 1B is a system for dynamic medical supply procurement that includes components that may be provided together or distributed. System 100B includes a central computer 110, a main memory 115, an administrator / provider computer 120, a first mobile computer 121, and a second mobile computer 122.

[0024]

[0034] Elements of FIG. 1B that are the same as FIG. 1A will not be reintroduced for brevity. The first mobile computer 121 includes a memory for storing instructions and a processor for executing instructions. The second mobile computer 122 includes a memory for storing instructions and a processor for executing instructions. Each of the first mobile computer 121 and the second mobile computer 122 represents a communication device, such as a mobile tablet computer, used by staff, such as nurses and technicians, during a medical procedure. The first mobile computer 121 and the second mobile computer 122 are used to input structured, formatted data and instructions, and / or unstructured, free-form data and instructions. As an example, staff can input clinical information into the first mobile computer 121 or the second mobile computer 122 during a medical procedure. Clinical information refers to clinical concepts and patient demographic information. As another example, staff can input operational information into the first mobile computer 121 or the second mobile computer 122 during a medical procedure. Operational information refers to information about the operation of a given hospital or department within a hospital. If the data and / or instructions are free-form, natural language processing may be applied to the data and / or instructions to interpret information from the medical procedure.

[0025]

[0035] FIG. 1C illustrates another system for dynamic medical supply procurement in accordance with a representative embodiment.

[0026]

[0036] 1C is a system for dynamic medical supply procurement that includes components that may be provided together or distributed. System 100C includes a central computer 110, a main memory 115, an administrator / provider computer 120, a first mobile computer 121, a second mobile computer 122, an imaging system 170, and a display 180.

[0027]

[0037] Elements of Figure 1C that are the same as Figure 1A and / or Figure 1B are not reintroduced for brevity. Figure 1C emphasizes the concept that the systems described herein may include more, fewer, and different elements than those shown in Figures 1A, 1B, and / or 1C. The functionality of the methods described herein is primarily due to central computer 110, although functionality of other elements in the systems of Figures 1A, 1B, and / or 1C may be invoked, along with other elements not shown.

[0028]

[0038] The central computer 110 can analyze clinical information entered into and received from the first mobile computer 121 or the second mobile computer 122 to determine compliance with clinical guidelines during the medical procedure. Additionally or alternatively, the central computer 110 can analyze operational information entered into and received from the first mobile computer 121 or the second mobile computer 122 to determine supply usage during the medical procedure.

[0029]

[0039] 1A, 1B, and / or 1C, the information received by the central computer 110 is obtained from a number of different sources over an electronic communications network. For example, the central computer 110 may be connected to a broadband cable, such as an Ethernet cable, to receive information from the medical procedure from the imaging system 170, the first mobile computer 121, the second mobile computer 122, or any or all of the various other types of electronic devices and systems that may be present in the medical procedure. The central computer 110 may process the information received from one or more sources to monitor the medical procedure and predict whether there is a need to obtain missing supplies.

[0030]

[0040] FIG. 1D illustrates a controller 150 for dynamic medical supply procurement according to a representative embodiment.

[0031]

[0041] The controller 150 includes a memory 151, a processor 152, a first interface 156, a second interface 157, a third interface 158, and a fourth interface 159. The memory 151 stores instructions. The processor 152 executes the instructions.

[0032]

[0042] The computers used to implement the controller 150 are the central computer 110, the administrator / provider computer 120, the first mobile computer 121, and / or the second mobile computer 122. The computers used to implement the controller 150 are illustrated in FIG. 4, although the controller 150 may include more or less elements than those illustrated in FIG. 1D or FIG.

[0033]

[0043] The first interface 156, the second interface 157, the third interface 158, and the fourth interface 159 may include ports, disk drives, wireless antennas, or other types of receiving circuits. The first interface 156, the second interface 157, the third interface 158, and the fourth interface 159 connect the controller 150 to other components, devices, and systems. For example, if the controller 150 is implemented in the central computer 110, these four interfaces connect the central computer to any four or more of the main memory 115, the administrator / provider computer 120, the first mobile computer 121 and the second mobile computer 122, the imaging system 170, and the display 180.

[0034]

[0044] Controller 150 may directly perform some of the operations described herein and indirectly perform other operations described herein. For example, controller 150 may indirectly control operations, such as by generating and transmitting content that is displayed on display 180. Thus, processes performed by controller 150 when processor 152 executes instructions from memory 151 may include steps that are not directly performed by controller 150.

[0035]

[0045] FIG. 1E illustrates an overview of a system for dynamic medical supply procurement according to a representative embodiment.

[0036]

[0046] The system overview of FIG. 1E includes five components, including component 1, component 2, component 3, component 4, and component 5. These components may all be implemented by the central computer 110 or distributed among the central computer 110 and the administrator / provider computer 120 and / or other components shown in FIG. 1A, FIG. 1B, and / or FIG. 1C. The components may be implemented or differentiated as different sets of software programs or subprograms, including programs and subprograms executed in parallel, such as by different cores of a multi-core processor. In FIG. 1E, component 1 is for data collection and preparation, component 2 is for generation of supply insights to improve clinical outcomes, component 3 is for generation of supply insights to improve operational and financial efficiencies, component 4 is for real-time forecasting to forecast additional supplies during a procedure, and component 5 is for a user interface to communicate and request supplies.

[0037]

[0047] In the system of FIG. 1E, data collection and preparation at 161 is component 1, which is based on data from a database and feedback from supply insight generation at 162 and real-time prediction at 164. Data collection and preparation at 161 provides data to the database, supply insight generation at 162, supply insight generation at 163, and real-time prediction at 164. For example, component 1 captures clinical, operational, and workflow information from multiple information systems within a given hospital or network of hospitals. Data used by component 1 comes from sources such as electronic medical records, radiology information systems, and cardiology information systems. The various sources providing data to component 1 are highly diverse, using different data types, data models, formats, and semantics. Component 1 may be responsible for interacting with various data sources to extract clinical and operational information related to a laboratory test. Interfacing with clinical, operational, and demographic data sources can be done using any of a variety of available state-of-the-art information technology communication protocols, such as HL7 (Health Level 7), DICOM (Digital Imaging and Communications in Healthcare), and FHIR (Fast Healthcare Interoperability Resources).

[0038]

[0048] As used herein, clinical information refers to clinical concepts and patient demographic information typically associated with clinical documents such as clinical notes, radiology reports, and medical histories. Examples of clinical concepts and patient demographic information captured by component 1 include demographic information (e.g., age, sex, race, height, weight), smoking history, medical history, history of chest pain, allergies (e.g., contrast allergy, drug allergy), pre-procedure vital signs (e.g., height, weight, blood pressure), and chronic conditions (e.g., diabetes).

[0039]

[0049] The clinical information used in component 1 may be further processed to capture clinical concepts present in the informatics medical system. Examples of clinical concepts include smoking history, echocardiography history, left ventricular (LV) exam history, enlarged LV exam history, cardiologist history, and ultrasound exam history that can be captured from the clinical information used in component 1. Some ontologies may define a published list of clinical concepts to be used in component 1. Examples of ontologies used to define clinical concepts include SNOMED CT (Systematized Nomenclature of Medical Care - Clinical Terminology) and Radlex (Radiology Information Vocabulary created by the North American College of Radiologists). As an example, natural language processing (NLP) algorithms using supervised and unsupervised approaches to identify clinical concepts in free text reports are used to capture clinical concepts. Another example is using RegEx (regular expression) expressions to query for clinical concepts in a database.

[0040]

[0050] Operational information refers to information regarding the operation of a given hospital or department within a hospital. Operational information can be automatically captured from electronic systems or manually entered using a user interface. Examples of information captured by component 1 include supplies used in the procedure (including the time the supplies were used), provider experience, provider preferences, contrast use, length of procedure, staff involved in the procedure such as nurses, technicians, operators, and anesthesiologists, type of access, location of procedure, type of procedure, and protocol used for the procedure.

[0041]

[0051] The information captured in components 1, 2, 3, 4, and 5 of FIG. 1E is stored in a relational or non-relational database.

[0042]

[0052] During the medical procedure, detailed information collected during the procedure may also be extracted, aggregated, and correlated with data obtained through component 1. Information collected during the procedure may include, but is not limited to, a sub-procedure log with detailed steps and times for each sub-procedure / step, identification of supplies used during the procedure, measurements taken during the procedure, vital signs taken during the procedure, and other information obtained during the procedure such as medical images. Information obtained during the procedure may be extracted from a CVIS (cardiovascular information system) and / or an EMR (electronic medical record system). Time stamps of any information obtained during the medical procedure may also be extracted. In this way, the workflow and progress of the medical procedure may be captured and information describing when supplies were used and at what stage may be mapped.

[0043]

[0053] Generation of supply insights at 162 is component 2 and is based on data from data collection and preparation at 161. Generation of supply insights at 162 is designed to improve clinical outcomes. Generation of supply insights at 162 provides supply insights to real-time predictions at 164 and to a user interface 165.

[0044]

[0054] Component 2 can analyze adherence to clinical guidelines for device usage and patient outcomes to identify physician training opportunities for improved outcomes. As better and newer medical devices are constantly being produced in the medical device field, using the most appropriate device based on clinical evidence may improve clinical outcomes for patients. There are several approaches to analyze clinically appropriate device usage. One analysis method is using clinical guidelines and user-defined pathways. Another analysis method is through data-driven modeling. The use of clinical guidelines and user-defined pathways can use published clinical guidelines on when and how to appropriately use a particular medical device based on clinical trial evidence showing better clinical outcomes.

[0045]

[0055] As an example of a process that includes component 2, in the presence of moderate or severe (less than 90%) stenosis, the use of a fractional flow reserve (FFR) device is recommended. This is because the use of an FFR device better guides decision-making in interventional medical procedures and improves patient outcomes. FFR is a measurement to determine the ratio of the maximum blood flow achievable in a diseased coronary artery to the theoretical maximum flow rate in a normal coronary artery. Users can also define pathways for when and how to use medical devices in their hospitals based on clinical guidelines and expert consensus. Data-driven modeling is used to suggest appropriate use of devices and is based on past clinical outcomes associated with device use. Machine learning or deep learning models can be trained to predict clinical outcomes using patient characteristics, surgical information, medical tests, diagnostic information, and device usage. Risk factors for adverse clinical outcomes can be included in the model to allow for fair comparison of device usage. Component 2 can also be used to suggest device usage.

[0046]

[0056] Generation of supply insights at 163 is component 3 and is based on data from data collection and preparation at 161. Generation of supply insights at 163 is intended to improve operational and financial efficiencies. Generation of supply insights at 163 also provides supply insights to real-time forecasts at 164 and to a user interface 165.

[0047]

[0057] Component 3 generates supply insights to improve operational and financial efficiencies. Component 3 uses information from component 1 to create insights into supply utilization, taking into account the financial aspects of a facility, such as a cath lab. By providing an overview of supplies used over a given period of time, healthcare providers and administrators are provided with the ability to understand supply utilization, patterns, trends, and cost reduction suggestions.

[0048]

[0058] Component 3 is relatively independent from other components, except for receiving input data from components 1 and 2. Alternatively, component 3 can get similar input from a database such as an inventory management system. Component 3 is realized with sub-components such as insight generation algorithm, user interface for administrator, and user interface for healthcare provider. The insight generation algorithm receives inputs such as supply usage, healthcare provider information, and treatment information from component 1, and can integrate / correlate this input for further analysis. Cost reduction can be analyzed using billing information or cost information. Matrices or key performance indicators can be used to reflect the provider's supply usage performance. Various techniques can be used for insight generation, such as time series analysis (for prediction / trend finding), machine learning, and deep learning. Root cause analysis and contributing factor analysis can also be applied.

[0049]

[0059] An example of an insight provided by the insight generation algorithm of component 3 is the creation of rules that prioritize more effective devices. That is, medical staff become accustomed to using some types of supplies and are often unaware of new supplies that are less expensive and more effective. The insight generation algorithm of component 3 can identify patterns of supply usage and financial and clinical outcomes associated with the patterns of supply usage, and benchmark the patterns of supply usage and financial and clinical outcomes in different user populations. Suggestions for financial improvement as well as a basis for eventual decision making can be provided. Another example of an insight provided by the insight generation algorithm is the identification of outliers in usage. Outliers in usage can be identified based on the staff involved in a given procedure. Supply information can be used in light of clinical and operational information. For example, an older patient with a history of heart failure and several comorbidities may require a higher level of care and supplies than a younger patient. Based on the insight generated by the insight generation algorithm of component 3, management may suggest additional training for medical staff.

[0050]

[0060] The Action Suggestions subcomponent of component 3 suggests actions to be taken to improve supply usage and reduce costs after current supply utilization pain points have been identified by component 3. Discrete event simulation (DES) and / or machine learning / deep learning methods can be used to predict outcomes with possible improvements if the suggestions are adopted. Discrete event simulation is a method of simulating the behavior and performance of a real process, facility, or system. DES models the process, equipment of the system as a series of "events" that occur over time. In the healthcare context, events include birth, admission to the intensive care unit (ICU), transfer, or discharge. Patients are modeled as independent entities, each of which can be given associated attribute information such as age, weight, location, etc., which can also be changed. DES simulation also considers resources such as beds, nursing staff, and equipment requirements. Thus, DES allows for the incorporation of complex decision-making logic that is not easily possible with other types of modeling. DES simulations also enable scenario testing to improve understanding of alternative ways in which new policies might be optimally met and can therefore be used to predict outcomes with possible improvements if proposals are adopted.

[0051]

[0061] The User Interface subcomponent of component 3 provides separate user interfaces for healthcare administrators and healthcare providers. For administrators, the healthcare administrator user interface provides an overview of supply usage, provider performance with respect to supply usage, and insights from component 3. A dashboard may consolidate the subcomponents to provide detailed options. Figure 5A shows an example of a healthcare administrator user interface, and Figure 5B shows an example of a provider user interface.

[0052]

[0062] Real-time prediction at 164 is component 4 and is based on data from data collection and preparation at 161, generate supply insights at 162, and generate supply insights at 163. The real-time prediction at 164 provides real-time prediction as output to a user or automated ordering system, such as for ordering supplies for immediate delivery. The real-time prediction at 164 also provides real-time prediction to the data collection and preparation at 161.

[0053]

[0063] Component 4 provides the ability to predict additional supplies during a procedure. Information from Component 1 can be used as a starting point to enable a healthcare provider to create an initial setup of supplies to be used in a given procedure. Due to the dynamic and complex nature of operating rooms such as cath labs, additional supplies that were not previously selected but are required may be identified as the medical procedure progresses. Component 4 captures clinical and operational information in real time during the medical procedure to predict additional supplies that will need to be obtained during the procedure. Component 4 may include three subcomponents for in-procedure data extraction and preparation, model training, and real-time prediction.

[0054]

[0064] Model training may use time series with multi-channel feature information from component 1, deep learning algorithms, time series analysis, or a combination of algorithms and analysis to train a classification model that includes the resulting supplies being used. An example of a deep learning algorithm is a recurrent neural network (RNN). Since the frequency of data varies for different features, data interpolation techniques may be applied before making a prediction. As an example, vital signs are collected more frequently than other types of data. Real-time prediction may include applying a model trained on historical data in real-time for a new procedure. Both the probability and confidence that the supplies will be used may be associated with each predicted supply, allowing the healthcare provider to determine whether the supplies need to be requested and / or prepared prior to the procedure.

[0055]

[0065] An example of the use of component 4 in a cath lab is for diagnostic cath labs. After diagnosis with x-ray and possibly other modalities such as FFR or wires, the physician is provided with the ability to decide whether to perform PCI (percutaneous coronary intervention) during the procedure. The stenosis level is measured and recorded along with the FFR value. Thus, the likelihood of using balloons and stents may be higher compared to a prediction based only on pre-procedure information. This allows for a more reliable prediction of the number of supplies required. As more PCI decision information is collected, the likelihood of needing more supplies may increase. The availability and reliability of supplies is continually updated as additional information is collected during the procedure. If a new device is predicted to be likely to be used, the nurse / physician can prepare the new device before it is needed, avoiding wasted waiting time, for example to prepare a third stent of a different size that will be needed in 10 minutes.

[0056]

[0066] A user interface 165 is component 5 and is based on data from the database and the generation of supply insights at 162 and 163. The user interface provides data to the database.

[0057]

[0067] Component 5 uses the information collected by components 1, 2, 3, and 4 to assist healthcare administrators in organizing and requesting supplies. Once supplies needed during a given procedure are predicted using the real-time model in component 4, component 5 can automatically send requests to add newly needed supplies in a timely manner. Knowing in real-time the quantities of supplies used during a given procedure and requesting supplies that would not otherwise be available improves workflow efficiency and avoids rescheduling of procedures due to supply shortages, thus improving clinical and financial outcomes.

[0058]

[0068] As an example of the use of Component 5, real-time supply predictions from Component 4 can be based on newly acquired x-ray and IVUS images during a diagnostic catheterization procedure to suggest the use of three drug-eluting stents (DES) (one 17 mm long DES and two 15 mm long DES). If only two acceptable DES are available in the current cath lab, another DES can be quickly shipped to the cath lab before the catheter is withdrawn. This avoids the need to schedule a step-by-step cath lab procedure to place the third stent.

[0059]

[0069] Another use of component 5 is to communicate with the physician during a medical procedure to suggest optimal supplies. Based on components 2, 3, and 4, component 5 can identify optimal supplies with acceptable clinical outcomes, efficient workflow, and reduced financial costs. To assist with supply decisions during surgery, suggestions are communicated to the operating clinician via the user interface. For example, the central computer 110 may notify the clinician at the first mobile computer 121 or the second mobile computer 122 to suggest an alternative imaging system 170 or simply to order additional quantities of a different type of supply.

[0060]

[0070] FIG. 2 illustrates a method for dynamic medical supply procurement according to a representative embodiment.

[0061]

[0071] The method of FIG. 2 may be performed by the system 100A of FIG. 1A, the system 100B of FIG. 1B, or the system 100C of FIG. 1C.

[0062]

[0072] 2 includes obtaining first clinical information and first operational information from a plurality of sources and a plurality of past medical procedures at S210. The central computer 110 establishes benchmarks from the plurality of past medical procedures. The benchmarks include types and quantities of supplies used in medical procedures having particular characteristics.

[0063]

[0073] The first clinical information includes many different types of information and results of the different types of information that are correlated. As an example, the use of a type of device by a clinician can be determined from a historical record of what type of device has been used by the clinician for a particular type of medical procedure. The controller 150 of the central computer 110 determines whether the clinician present during the medical procedure is familiar with the supplies that will be used during the medical procedure.

[0064]

[0074] At S215, a clinician history record is obtained. For example, the subject's clinical history record may be obtained from an EMR system or another type of system that stores a subject's past clinical records of medical procedures.

[0065]

[0075] The method of Figure 2 includes storing, at S220, identities of supplies used during the medical procedure. S220 occurs prior to the medical procedure, such as when the medical procedure is planned. Thus, supplies used during the medical procedure are identified prior to the medical procedure and stored at S220.

[0066]

[0076] 2 includes monitoring information from the medical procedure in S230. The monitoring in S230 may be performed directly by the central computer 110 or indirectly from information from medical systems such as the imaging system 170, from mobile devices such as the first mobile computer 121 and the second mobile computer 122, and from other sources such as sensors and monitors. The monitoring information is provided to the central computer 110 for analysis to ensure the suitability of the supplies identified for the medical procedure in S220.

[0067]

[0077] When the information from the medical procedure is from imaging system 170, the information from the medical procedure is based on images of the subject of the medical procedure taken during the medical procedure and processed in real time. A simple use case of the subject's images is when the information indicates low contrast in the image, which necessitates the immediate procurement of a replacement imaging system of the same or a different type. In this example, supplies that are missing during the medical procedure are procured based on the quality of the subject's images, and the missing but procured supplies include a replacement imaging device.

[0068]

[0078] The method of Figure 2 includes predicting, at S240, whether missing supplies will need to be obtained during the medical procedure. The prediction, at S240, may be made periodically or continuously during the medical procedure while information from the medical procedure is being monitored, at S230. The prediction, at S240, may be made for any type of supply used during the medical procedure, or may be limited to only those types of supplies designated as critical to the medical procedure.

[0069]

[0079] If, at S240, a need to obtain supplies is predicted, the method of Figure 2 includes, at S250, obtaining the supplies either by immediately retrieving the supplies and alerting a staff member to deliver the supplies to the medical procedure or by generating a request for immediate delivery of the supplies to an automated system.

[0070]

[0080] FIG. 3A illustrates a method for dynamic medical supply procurement according to a representative embodiment.

[0071]

[0081] The method of FIG. 3A is performed during or after S230 of FIG.

[0072]

[0082] The method of Figure 3A includes analyzing second clinical information at S232. The second clinical information is from a medical procedure, such as clinical information generated during and based on the medical procedure. The second clinical information may be information from a medical system, such as imaging system 170, information from a mobile device, such as first mobile computer 121 or second mobile computer 122, and information from other sources, such as sensors and monitors.

[0073]

[0083] 3A includes analyzing second operational information at S234. The second operational information is from the medical procedure, such as clinical information generated during and based on the medical procedure. The analysis at S234 is performed by the central computer 110.

[0074]

[0084] 3A includes comparing the second clinical information from S232 and the second operational information from S234 to clinical guidelines, machine learning models, and / or user-defined rules at S236. The comparison at S236 is performed by the central computer 110. The comparison may be performed to determine if the medical procedure is outside of clinical guidelines, if the machine learning model indicates that supplies for the medical procedure need to be obtained, or if the second clinical information indicates that the clinical procedure violates a user-defined rule.

[0075]

[0085] FIG. 3B illustrates insight generation for dynamic medical supply procurement according to a representative embodiment.

[0076]

[0086] Insight generation for clinical outcome improvement is based on clinical guidelines. Insight generation is performed by component 2 of FIG. 1E (i.e., supply insight generation at 162). As an example, in FIG. 3B, a decision tree reflects clinical guidelines for appropriate use of fractional flow reserve (FFR) devices. Device utilization information indicates physicians opportunities for appropriate utilization improvement. For example, physicians A, B, and C may increase FFR device utilization in accordance with clinical guidelines, which may help improve patient outcomes, while physician E may decrease FFR device utilization in accordance with clinical guidelines.

[0077]

[0087] In FIG. 3B, at the top level of the decision tree, clinical findings are shown. At the second level, the presence or absence of symptoms is confirmed. At the third level on the left, the measurement B is compared with a threshold C to determine whether device D is recommended at the fourth level. At the third level on the right, the medical image is determined to show characteristic E to determine whether device D is recommended. In other words, while the medical procedure is being performed, interactive feedback from the medical procedure is compared with clinical guidelines to determine whether device D needs to be present. The interactive feedback is automatically collected and analyzed information, for example, medical images from the imaging system 170 analyzed by the central computer 110. The interactive feedback may also be information collected from staff attending the medical procedure, for example, staff using the first mobile computer 121 or the second mobile computer 122.

[0078]

[0088] As an example of FIG. 3B, the clinical findings shown at the top are fed into the decision tree. For example, the first decision from the clinical findings is whether the medical procedure involves stable angina or unstable angina. If the first decision determines that the angina is stable, the second decision is whether the lesion is moderate or severe. If the first decision determines that the angina is unstable, the second decision is whether the lesion is a culprit lesion. Clinical guidelines are applied at the third or fourth level to indicate whether FFR is recommended. If FFR is recommended, the results are compared with the clinical guideline of whether FFR is 0.80 or less, and the results are used to determine whether percutaneous coronary intervention (PCI) or optical medical therapy (OMT) is applied.

[0079]

[0089] The insight generation of Figure 3B represents a process that occurs for multiple supplies for any particular medical procedure. The insight generation of Figure 3B may also be performed repeatedly for a single supply for any particular medical procedure, for example periodically or whenever an event triggers insight generation.

[0080]

[0090] FIG. 4 illustrates a computer system upon which a method for dynamic medical supply procurement according to another representative embodiment is implemented.

[0081]

[0091] 4, computer system 400 includes a set of executable software instructions to cause computer system 400 to perform any of the methods or computer-based functions disclosed herein. Computer system 400 may operate as a stand-alone device or may be connected to other computer systems and peripheral devices, for example, using network 401. In an embodiment, computer system 400 performs logical processing based on digital signals received via an analog-to-digital converter.

[0082]

[0092] In a networked arrangement, the computer system 400 operates as a server or client user computer in a server-client-user network environment, or as a peer computer system in a peer-to-peer (or distributed) network environment. The computer system 400 may also be implemented as or incorporated into a variety of devices, such as the central computer 110, the first mobile computer 121, the second mobile computer 122, the administrator / provider computer 120, or any other machine capable of executing a set of software instructions (e.g., sequential) that specify actions that the machine should take. The computer system 400 may be implemented as or incorporated into a device in an integrated system that includes additional devices. In an embodiment, the computer system 400 may be implemented using electronic devices that provide voice, video, or data communication. Additionally, although the computer system 400 is described in the singular, the term "system" is intended to include a collection of systems or subsystems that individually or jointly execute one or more sets of software instructions to perform one or more computer functions.

[0083]

[0093] As shown in FIG. 4, the computer system 400 includes a processor 410. The processor 410 may be considered as a representative example of the processor 152 of the controller 150 of FIG. 1D, and executes instructions to implement some or all aspects of the methods and processes described herein. The processor 410 is tangible and non-transient. As used herein, the term "non-transient" is interpreted as a property of a state that persists for a period of time, rather than an eternal property of a state. The term "non-transient" specifically negates momentary properties, such as carrier waves or signals or other forms of properties that exist only temporarily at any time and place. The processor 410 is an article of manufacture and / or a machine part. The processor 410 executes software instructions to perform the functions described in various embodiments herein. The processor 410 may be a general-purpose processor or part of an application-specific integrated circuit (ASIC). The processor 410 may also be a microprocessor, a microcomputer, a processor chip, a controller, a microcontroller, a digital signal processor (DSP), a state machine, or a programmable logic device. The processor 410 may also be a logic circuit, such as a programmable gate array (PGA), such as a field programmable gate array (FPGA), or another type of circuit including discrete gate and / or transistor logic. The processor 410 may be a central processing unit (CPU), a graphics processing unit (GPU), or both. Additionally, any processor described herein may include multiple processors, parallel processors, or both. Multiple processors may be included or coupled in a single device or multiple devices.

[0084]

[0094] The term "processor" as used herein encompasses an electronic component capable of executing a program or machine-executable instructions. References to a computing device that includes a "processor" should be interpreted to include multiple processors or processing cores, such as in a multi-core processor. A processor may also refer to a collection of processors within a single computer system or a collection of processors distributed across multiple computer systems. The term "computing device" should also be interpreted to include a collection or network of computing devices, each of which includes one or more processors. A program comprises software instructions that are executed by one or more processors, either within the same computing device or distributed among multiple computing devices.

[0085]

[0095] The computer system 400 further includes a main memory 420 and a static memory 430. The memories of the computer system 400 communicate with each other and with the processor 410 via a bus 408. Either or both of the main memory 420 and the static memory 430 may be considered representative of the memory 151 of the controller 150 of FIG. 1B, storing instructions used to implement some or all aspects of the methods and processes described herein. The memory described herein is a tangible storage medium for storing data and executable software instructions, and is non-transient while the software instructions are stored therein. As used herein, the term "non-transient" is to be interpreted as a property of a state that persists for a period of time, rather than a permanent property of a state. The term "non-transient" specifically negates properties that are momentary, such as a carrier wave or signal or other type of property that exists only temporarily at any time and place. The main memory 420 and the static memory 430 are articles of manufacture and / or machine parts. The main memory 420 and the static memory 430 are computer-readable media from which a computer (such as the processor 410) can read data and executable software instructions. Each of the main memory 420 and the static memory 430 may be implemented as one or more of a random access memory (RAM), a read-only memory (ROM), a flash memory, an electrically programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a register, a hard disk, a removable disk, a tape, a compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a floppy disk, a Blu-ray disk, or any other form of storage medium known in the art. The memory may be volatile or non-volatile, secure and / or encrypted, non-secure and / or non-encrypted.

[0086]

[0096] "Memory" is an example of a computer-readable storage medium. Computer memory is any memory that is directly accessible to a processor. Examples of computer memory include, but are not limited to, RAM memory, registers, and register files. References to "computer memory" or "memory" should be interpreted as referring to multiple memories. For example, a memory may be multiple memories within the same computer system. A memory may also be multiple memories distributed among multiple computer systems or devices.

[0087]

[0097] As shown, computer system 400 further includes an image / video display unit 450, such as a liquid crystal display (LCD), an organic light emitting diode (OLED), a flat panel display, a solid state display, or a cathode ray tube (CRT). Additionally, computer system 400 includes an input device 460, such as a keyboard / virtual keyboard, a touch input screen, a voice input with voice recognition, and a cursor control device 470, such as a mouse or a touch input screen or pad. Computer system 400 also optionally includes a disk drive unit 480, a signal generating device 490, such as a speaker or a remote control, and / or a network interface device 440.

[0088]

[0098] In an embodiment, as shown in FIG. 4, the disk drive unit 480 includes a computer readable medium 482 having one or more sets of software instructions 484 (software) embedded therein. The set of software instructions 484 is read from the computer readable medium 482 for execution by the processor 410. Furthermore, the software instructions 484, when executed by the processor 410, perform one or more steps of the methods and processes described herein. In an embodiment, the software instructions 484 reside in whole or in part in the main memory 420, the static memory 430, and / or the processor 410 during execution by the computer system 400. Furthermore, the computer readable medium 482 may include the software instructions 484 or may receive and execute the software instructions 484 in response to a propagated signal to enable devices connected to the network 401 to communicate voice, video, or data over the network 401. The software instructions 484 may be transmitted or received over the network 401 via the network interface device 440.

[0089]

[0099] In embodiments, dedicated hardware implementations, such as application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), programmable logic arrays, and other hardware components, are constructed to perform one or more of the methods described herein. One or more embodiments described herein may implement functionality using two or more specific interconnected hardware modules or devices, with associated control and data signals that can be communicated between the modules. Thus, the present disclosure encompasses software, firmware, and hardware implementations. Nothing in this application should be construed as being implemented or capable of being implemented using only software rather than hardware, such as a tangible non-transitory processor and / or memory.

[0090]

[0100] In accordance with various embodiments of the present disclosure, the methods described herein may be implemented using a hardware computer system executing a software program. Furthermore, in non-limiting exemplary embodiments, implementations such as distributed processing, component / object distributed processing, and parallel processing are possible. A virtual computer system process may implement one or more of the methods or functions described herein. The processors described herein may also be used to support virtual processing environments.

[0091]

[0101] FIG. 5A illustrates a user interface for an administrator / provider computer for dynamic medical supply procurement according to a representative embodiment.

[0092]

[0102] In FIG. 5A, the user interface 615A is for an administrator. The user interface 615A is provided via the administrator / provider computer 120. The user interface 615A presents information for the example period July 1, 2019 to June 30, 2020. The information includes a selection of case types and a supply summary for 68 cases with a quantity bar on the left and a case bar on the right for each set of supplies. The information also includes usage, trends, and insights for different supplies by provider. The insights in FIG. 615A show the administrator the potential supply cost savings if waste is eliminated, if supplies nearing their expiration date are managed, and if specific individual providers (staff members) are educated to use the less expensive supply A instead of the more expensive supply B.

[0093]

[0103] FIG. 5B illustrates another user interface for an administrator / provider computer for dynamic medical supply procurement according to a representative embodiment.

[0094]

[0104] In FIG. 5B, user interface 615B is for a donor. User interface 615B is provided via administrator / donor computer 120. User interface 615B presents information for the period July 1, 2020 to June 30, 2020, as an example. The information includes a selection of daily supply usage and the percentage of supplies used or wasted each day. User interface 615B also shows a comparison of the donor's supply performance score within the donor's organization, so compared to the donor's peers. User interface 615B also displays suggested actions, such as switching to less expensive supplies or using supplies that are nearing their expiration date. User interface 615B also shows supplies that are expiring within one month, including the relative number of such supplies compared to the total number.

[0095]

[0105] Dynamic supply procurement therefore allows customization of supply delivery in real time according to staff preferences and capabilities, and patient condition. Real-time prediction of supply requirements during procedures is supported by medical data such as medical images and physiological information from procedures. Dynamic supply procurement can be implemented in hospital and departmental electronic systems such as hospital information systems and cardiovascular information systems.

[0096]

[0106] Although dynamic medical supply sourcing has been described with reference to certain exemplary embodiments, it is understood that the words used herein are words of description and illustration, rather than words of limitation. Changes may be made in its aspects within the purview of the appended claims, as currently presented and as amended, without departing from the scope and spirit of dynamic medical supply sourcing. Although dynamic medical supply sourcing has been described with reference to particular means, materials, and embodiments, dynamic medical supply sourcing is not intended to be limited to the details disclosed. Rather, dynamic medical supply sourcing extends to all functionally equivalent structures, methods, and uses as fall within the scope of the appended claims.

[0097]

[0107] The illustrations of the embodiments described herein are intended to provide a general understanding of the structure of various embodiments. The illustrations do not completely describe all elements and features of the disclosure described herein. Many other embodiments will be apparent to those skilled in the art upon review of the present disclosure. Other embodiments can be utilized and derived from the present disclosure, such as structural and logical substitutions and changes can be made without departing from the scope of the present disclosure. Moreover, the illustrations are merely representative and may not be to scale. Certain proportions in the illustrations may be exaggerated and other proportions may be minimized. Thus, the present disclosure and the figures should be considered illustrative and not restrictive.

[0098]

[0108] One or more embodiments of the present disclosure may be referred to herein, individually and / or collectively, by the term "invention", for convenience only and without any intention to spontaneously limit the scope of the present application to any particular invention or inventive concept. Also, although specific embodiments are illustrated and described herein, it should be understood that subsequent arrangements designed to achieve the same or similar purpose may be substituted for the specific embodiment shown. The present disclosure is intended to cover any and all subsequent adaptations or variations of the various embodiments. Combinations of the above embodiments with other embodiments not specifically described herein will be apparent to those of skill in the art upon review of the description.

[0099]

[0109] The Abstract of the Disclosure is provided to comply with 37 CFR Rule 1.72(b) and is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, the above Detailed Description may group or describe various features in a single embodiment for the purpose of streamlining the disclosure. This disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter may be directed to less than all features of any of the disclosed embodiments. Accordingly, the following claims are incorporated into the Detailed Description, with each claim standing on its own as defining separate claimed subject matter.

[0100]

[0110] The foregoing description of the disclosed embodiments is provided to enable any person skilled in the art to practice the concepts described in the present disclosure. Therefore, the above disclosed subject matter is considered to be illustrative and not restrictive. Moreover, the appended claims are intended to cover all such modifications, enhancements, and other implementations that fall within the true spirit and scope of the present disclosure. Thus, to the maximum extent permitted by law, the scope of the present disclosure shall be determined by the broadest permissible interpretation of the following claims and their equivalents, and shall not be limited or restricted by the foregoing detailed description.

Claims

1. A method for dynamically obtaining medical supplies, comprising: storing in a main memory the identification information of the medical supplies used during the medical procedure; monitoring information from the medical procedure during the medical procedure; predicting, by a processor executing instructions and based on monitoring the information, whether a missing medical supply will need to be obtained during the medical procedure; generating an alert during the medical procedure indicating the medical supplies are out of stock based on the predicted need to obtain the medical supplies that are out of stock during the medical procedure; A method comprising:

2. 10. The method of claim 1, further comprising analyzing, at a computer, clinical information entered into and received from a communication device to determine compliance with clinical guidelines during the medical procedure.

3. 10. The method of claim 1, further comprising analyzing, at a computer, operational information entered into and received from a communication device to determine medical supply usage during the medical procedure.

4. a central computer including a first memory storing first instructions and a first processor executing said first instructions; a main memory for storing identification information of medical supplies used during a medical procedure; 1. A system for dynamically obtaining medical supplies, comprising: When executed in the first memory, the first instructions cause the central computer to: monitoring information from said medical procedure during said medical procedure; predicting whether a medical supply will need to be obtained during the medical procedure based on monitoring the information; The system obtains the medical supplies that are out of stock during the medical procedure based on a prediction that the medical supplies that are out of stock will need to be obtained during the medical procedure.

5. When executed by the first processor, the first instructions further cause the central computer to: capturing first data from a plurality of sources prior to the medical procedure to obtain first clinical information and first operational information used to determine the medical supplies used during the medical procedure; 5. The system of claim 4, wherein the information from the medical procedure is monitored during the medical procedure to obtain second clinical information and second operational information to predict whether the medical supply will need to be obtained if it is out of stock during the medical procedure.

6. When executed by the first processor, the first instructions further cause the central computer to: comparing the information from the medical procedure with clinical guidelines; determining whether the medical supplies used during the medical procedure comply with the clinical guidelines based on a comparison of the information from the medical procedure and the clinical guidelines; 5. The system of claim 4, further comprising obtaining the medical supplies missing during the medical procedure based on determining that the medical supplies used during the medical procedure do not comply with the clinical guidelines.

7. and a mobile computer including a second memory storing second instructions and a second processor executing the second instructions, the second instructions, when executed by the second processor, causing the mobile computer to: The system of claim 4 , further comprising: acquiring said information from said medical procedure during said medical procedure; and transmitting said information from said medical procedure to said central computer.

8. When executed by the first processor, the first instructions further cause the central computer to: retrieve information from past medical procedures, establishing benchmarks from a plurality of said past medical procedures; 5. The system of claim 4, wherein the prediction of whether missing medical supplies will need to be obtained during the medical procedure is further based on the benchmarks from the plurality of past medical procedures.

9. When executed by the first processor, the first instructions further cause the central computer to: The system of claim 4 , further comprising automatically generating and transmitting a request for the missing medical supplies to obtain the missing medical supplies.

10. The system of claim 4 , wherein the information from the medical procedure is based on images of a subject of the medical procedure taken during the medical procedure and processed in real time.

11. The system of claim 4 , wherein the information from the medical procedure is obtained from a plurality of different sources via an electronic communications network.

12. a memory for storing instructions; a processor for executing said instructions; A controller comprising: When executed by the processor, the instructions cause the controller to: Capture the identity of medical supplies used during the medical procedure; monitoring information from said medical procedure during said medical procedure; predicting whether a medical supply will need to be obtained during the medical procedure based on monitoring the information; The controller generates an alert during the medical procedure indicating the medical supplies are missing based on predicting a need to obtain the missing medical supplies during the medical procedure.

13. When executed by the processor, the instructions further cause the controller to: obtaining images of the subject of the medical procedure taken during the medical procedure and processed in real time; The controller of claim 12 , further comprising: obtaining the medical supplies missing during the medical procedure based on the quality of the image of the subject; and the missing medical supplies including medical imaging equipment.

14. When executed by the processor, the instructions further cause the controller to: determining whether a clinician present during the medical procedure is familiar with the medical supplies used during the medical procedure; 13. The controller of claim 12, wherein the controller causes the medical supplies missing during the medical procedure to be obtained based on the clinician's familiarity with the medical supplies used during the medical procedure.

15. When executed by the processor, the instructions further cause the controller to: The controller of claim 14 , wherein natural language processing is used to interpret the information from the medical procedure.

Images